This is the official repository for the paper
"Moser Flow: Divergence-based Generative Modeling on Manifolds" [arxiv]
create a conda enviorenment, we used python 3.7. Then run ''' bash install_requirements.sh '''
For the earth datasets unzip data/earth_data.zip
All results are stored by default in a directory ./_experiments
To reproduce the toy data experiments, run the following example cmd line
bash scripts/run_toy_data.sh
To run Moser Flow on the high resolution example, run
bash scripts/run_high_density_moser.sh
To run FFJORD on the high resolution example, run
bash scripts/run_high_density_ffjord.sh
These scripts will run the experiments and save checkpoints during the time processes, and no plots will be generated. To compare the results of the two experiments use evaluate_time.py. For arguments definitions run ''' python evaluate_time.py -h '''
To run the earth data experiments
bash scripts/run_earth_data.sh
The code that visualizes the resulting densities is property of Facebook, hence unavailable. We present only the likelihood results.
To learn the signed distance function of the stanford bunny, run
python eikonal/implicit_network_3d.py --pc_path eikonal/deep_geometric_prior_data/standford/bun_zipper.ply --name bunny
To run the experiment on the Stanford bunny, update the correct path to the saved checkpoint of the SDF in configs/surface.yml and run
bash scripts/run_surface.sh